I will propose a method of command line completion based on
a probabilistic model. This method supplements the existing
deterministic ones.
The probabilistic models that are used, are developed within
the context of imprecise probabilities and are the main focus
of my research.
The models are variants of the imprecise Dirichlet model. They
are used to represent the assessments about all possible
completions. Additionally, they allow for learning by observing
the commands typed previously.
Because I use an imprecise probabilistic model, a partial
(instead of a linear) ordering of the possible completion
actions will be constructed during decision making.